Data is playing a major role in the growth of any business exponentially. For the data to be understood with its trends, it requires lots of analysis and research. It requires special skills which help in understanding the pattern of data and to come to a conclusion that how will the data lead to a growth of business and how changing functionalities will bring in the necessary change. This job is mutually done by data scientists and business analysts. Though both these roles help in the expansion of any field, they both Data Scientist vs Business Analyst have their own roles and responsibilities which differ in their own ways. Although the main motto of both these jobs is business growth, the variance in the actual work that they do will be seen further.
Business analysts are responsible for a range of tasks including understanding business requirements, laying out plans and developing actionable insights. Data scientists, on the other hand, are professionals responsible for analysing, preparing, formatting, and maintaining information. Business analysis combines integrative skills like analytics, business acumen and domain knowledge, whereas Data science involves skills pertaining to computer science, mathematics and statistics.
Business analysts are professionals who look into the ever changing needs of any business and assist them in implementing those changes. They form a bridge of communication between various departments in a business organisation to execute any business plan.
Data scientists are responsible for developing algorithms and drawing data inferences. Since data science aims at unveiling complex data patterns by studying and understanding data sets, it is important that data scientists are well versed in multidisciplinary skill sets.
Both these roles are in fact, similar in a lot of ways, since both involve data gathering, inference accumulation and data modelling. The scope of data science and business analytics often overlap and the skill sets are not mutually exclusive. In any business environment, data scientists and business analysts work closely to understand and implement strategies. However, there are certain differences between these two branches that aspiring professionals must consider to understand which is best suited for them. Typically, data science can be taken up by early career professionals but business analytics is better suited for professionals with experience in business development, technology and project management.
If there’s one thing that has emerged as a force to be reckoned with in the world today – it’s data. Data is driving and shaping modern businesses exponentially. However, data in itself doesn’t hold much value for businesses unless it is analysed and categorised. Experts who dabble in data analytics can either be from a data science or a business analytics background. While both data scientists and business analysts are often seen working in close collaboration in a data driven environment, each of the roles involves different tasks and responsibilities.
Both data science and business analytics are popular career choices for young professionals today. If the myriad ways in which data work fascinates you then you can choose from either of the two career paths after considering your educational background, experience, skills and interests. To help you choose a career path, we have listed down the essentials and requirements of each of these roles.
Job Responsibilities in Business Analyst
- Engage with our existing and prospective customers and help them to adopt products and solutions to meet their business requirements
- Ensure consistent growth in product awareness, adoption and usage by customers
- Showcase product and solution concepts via presentations, demos, user evangelization and effective documentation
- Lead discovery sessions with IT and business users to understand the client’s business objectives and system/application needs
- With an excellent understanding of product features and related technologies, design the solution that best meets the client’s requirements
- Proactively create documentary artifacts like business cases, usage scenarios, solution blueprints, FAQs, meeting notes… etc.
- Lead or work with other customer success teams to ensure successful completion of project milestones for production and the initial rollout phase of the project
- Communicate progress and expectations, escalate problems for awareness and resolution
- Lead client training sessions
- Support clients and play a key role in promoting solution adoption and usage
- Provide regular and adequate end user feedback to the product team
Job Responsibilities of Data Scientist
- Demonstrate and drive deep technical expertise in solving real world retail business problems through the application of machine learning
- Collaborate with other team members both within and outside the data science team to create and deliver world class data science products
- Act as an SME on the floor and help build data science capabilities
- Preparing monthly sprint plans, prioritising requests from partner product teams
- Partnering with the product team to create key performance indicators and new methodologies for measurement
- Translating data into actionable insights for the stakeholders
- Automate reporting for weekly business metrics, identify areas of opportunity to automate and scale ad-hoc analyses.
Data Scientist vs Business Analyst Salary
Data Scientist Salary
Data scientist salaries in India start from ₹500,000 per annum and go upto more than ₹ 2,000,000 per annum. Depending on the number of years of experience and skill set of the data science professional.
Freshers with 1 to 5 years of experience can expect to earn around ₹600,000-700,000 per annum.
Mid-level data scientists with 5 to 10 years of experience can expect to earn close to ₹1,100,000 per annum while senior data scientists with more than 10-12 years of experience can expect anything around ₹2,000,000 per annum.
Business Analyst Salary
The average salary of a business analyst in India is around ₹700,000 per annum. As with any other domain, business analysts’ salaries depend on the years of experience and the extent of the expertise.
An Entry level business analyst with 1 to 2 years experience can expect to earn anything around ₹ 600,000 per annum while a mid-level analyst can earn anything from ₹800,000-11,148,110 per annum. Senior business analysts with 10 or more years of experience can earn anything around ₹1,800,000-2,200,000 per annum.
With both data scientists and business analyst, the recruiting company also makes a difference. Companies like Accenture, Cognizant, Mu Sigma, JP Morgan seems to be the top companies to work with.
Difference between Business Analyst and Data Analyst
The difference between Business Analysts and Data Analysts is primarily based on how each of them deal with data. There are, in fact, quite a lot of similarities between these two roles, and depending on the company size, these roles can be interchangeable. However, there are certain data specific functions that are unique to each analyst role. Let’s look into those.
Business analysts deal with business implications of data and how to use them in any business environment to achieve the desired results. Data analysts, on the other hand, primarily analyse data to identify and reveal patterns, draw conclusions and insights from random data. In a way, one could say that the reports created by data analysts help businesses analysts in supporting their business decisions.
Even from a skillset point of view, there are differences between business analysts and data analysts. Data analysts need to know data science, data mining, data modelling, basic statistics, and maybe even big data analytics. Business analysts, on the other hand, need to know how to grow any business, apart from knowing the data skills.
Business analysts need to know how to grow business
Data analysts need to know how to use data for business ends
Business analyst responsibilities include project management, stakeholder communication, quality testing, creating business cases and more
Data Analyst responsibilities include data entry, complex calculations, extrapolation and interpretation, troubleshooting, and more
Business Analysts generally have a background in business studies
Data analysts generally have a background in statistics and data science
Data scientists and business analysts are expected to constantly upskill and keep abreast of the latest technologies and developments in their respective fields. Clearly, the decision cannot be an impulsive one. Refer to the curriculum of data science and business analysis for further details so that you are certain of the path you choose.
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